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Congenital anomalies and health outcomes among survivors of childhood cancer: A report from the childhood cancer survivor study.

2023· article· en· W4379282619 on OpenAlexaff
Amanda E. Janitz, Weiyu Qiu, Jeremy M. Schraw, Sogol Mostoufi‐Moab, Douglas R. Stewart, Kevin C. Oeffinger, Joseph Philip Neglia, Lucie M. Turcotte, Smita Bhatia, Wendy M. Leisenring, Yutaka Yasui, Gregory T. Armstrong, Philip J. Lupo

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of Alberta
FundersNational Institutes of HealthAmerican Lebanese Syrian Associated Charities
KeywordsMedicineHazard ratioPediatricsCancerCommon Terminology Criteria for Adverse EventsConfidence intervalInternal medicine

Abstract

fetched live from OpenAlex

e22021 Background: Although congenital anomalies are established risk factors for developing childhood cancer, less is known about health outcomes among survivors of childhood cancer with known congenital anomalies. Therefore, we estimated the risk of chronic health conditions (CHCs) and subsequent malignant neoplasms (SMNs) among survivors of childhood cancer by congenital anomaly status in the Childhood Cancer Survivor Study (CCSS). Methods: Overall, 22,247 five-year survivors self-reported congenital anomalies. We included 16 conditions present at birth, excluding self-reported genetic conditions. Patient-reported CHCs were graded using the Common Terminology Criteria for Adverse Events (CTCAE v. 4.03). Self-reported SMNs were confirmed through pathology report review. Hazard ratios (HR) of CHCs comparing survivors with vs. without congenital anomalies and their 95% confidence intervals (CI) were estimated using Cox regression with age as the time scale, adjusted for race/ethnicity, education, household income, health insurance, and original cancer treatment. Results: Among survivors (median age at cancer diagnosis 6 years (range 3-12), age at follow-up 31 years (range 25-39)), 16.9% (n = 3880) reported a congenital anomaly. The most common anomalies included: large/multiple birth marks (n = 1540, 40.4%); congenital heart defects (n = 693, 18.4%); and kidney, bladder, or genital abnormalities (n = 607, 14.6%). Relative to survivors without anomalies, those with anomalies more commonly had astrocytoma, Wilms tumor, and neuroblastoma primary cancers (p < 0.001), treated with radiation (p = 0.011), cyclophosphamide (p < 0.001), anthracyclines (p < 0.001), and epipodophyllotoxins (p = 0.044), and younger age at cancer diagnosis (11.3% vs. 5.7% diagnosed prior to one year of age, p < 0.001). Survivors with anomalies were more likely to develop any CHC (CTCAE grades 1-5 HR: 1.31, 95% CI: 1.23, 1.39); severe, disabling, life-threatening or fatal CHCs (grades 3-5 HR: 1.42 (95% CI: 1.28, 1.58); and multiple CHCs (≥2 conditions HR: 1.36, 95% CI: 1.27, 1.46; ≥3 conditions HR: 1.49, 95% CI: 1.37, 1.63). Survivors with anomalies had increased risk for adverse outcomes across multiple systems (all p < 0.001), including: hearing/vision/speech (HR: 1.37); urinary (HR: 1.42); hormonal/endocrine (HR: 1.24); heart/circulatory (HR: 1.35); digestive (HR: 1.49); and brain systems (HR: 1.42). We observed no differences in the risk of any SMN by congenital anomaly status (HR: 1.12, 95% CI: 0.90, 1.38). Conclusions: In our assessment, survivors of childhood cancer with self-reported congenital anomalies had an increased risk of developing CHCs compared to survivors without reported anomalies, but no increased risk of SMNs. Evaluating congenital anomalies allows identification of a population of childhood cancer survivors at high risk for poor long-term health outcomes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.151
GPT teacher head0.495
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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